Forecasting TAIFEX based on fuzzy time series and particle swarm optimization

  • Authors:

  • I-Hong Kuo;Shi-Jinn Horng;Yuan-Hsin Chen;Ray-Shine Run;Tzong-Wann Kao;Rong-Jian Chen;Jui-Lin Lai;Tsung-Lieh Lin

  • Affiliations:

  • Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Computer Science & Information Engineering, National Taiwan Univers ...;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, Technology and Science Institute of Northern Taiwan, Taipei, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Electronic Engineering, Technology and Science Institute of Norther ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

Quantified Score

Hi-index 12.06

Visualization

Abstract

The TAIFEX (Taiwan Stock Index Futures) forecasting problem has attracted some researchers' attention in the past decades. Several forecast methods for the TAIFEX forecasting based either on the statistic theorems have been proposed, but their results are not satisfied. Fuzzy time series is used to doing forecasting but the forecasted accuracy still needs to be improved. In this paper we present a new hybrid forecast method to solve the TAIFEX forecasting problem based on fuzzy time series and particle swarm optimization. The experimental results show that the new proposed forecast model is better than any existing fuzzy forecast models and is more precise than four famous statistic forecast models.